class_docstr: Principal Component Analysis (PCA).
  
  Parameters
  ----------
  rescale_with_mean
      Whether or not to subtract each column's mean before performing SVD.
  rescale_with_std
      Whether or not to standardize each column before performing SVD.
  n_components
      The number of principal components to compute.
  n_iter
      The number of iterations used for computing the SVD.
  copy
      Whether nor to perform the computations inplace.
  check_input
      Whether to check the coherence of the inputs or not.
docstr: Returns the row principal coordinates.
  
  The row principal coordinates are obtained by projecting `X` on the right eigenvectors.
  
  Synonyms
  --------
  Row projections
  Factor scores
  Loadings
docstr_tok: ["Returns","the","row","principal","coordinates",".","The","row","principal","coordinates","are","obtained","by","projecting","`","X","`","on","the","right","eigenvectors",".","Synonyms","--","--","--","--","Row","projections","Factor","scores","Loadings"]
code: def row_coordinates(self, X: pd.DataFrame):
      """Returns the row principal coordinates.
  
      The row principal coordinates are obtained by projecting `X` on the right eigenvectors.
  
      Synonyms
      --------
      Row projections
      Factor scores
      Loadings
  
      """
  
      index = X.index if isinstance(X, pd.DataFrame) else None
      X = self._scale(X)
      X = np.array(X, copy=self.copy)
  
      coord = pd.DataFrame(data=X.dot(self.svd_.V.T), index=index)
      coord.columns.name = "component"
      return coord
  
imports: from __future__ import annotations
  import functools
  import altair
  import numpy
  import pandas
  import sklearn.base
  import sklearn.utils
  from sklearn import preprocessing
  from prince import svd, utils
label_desc: Use image node_id 6 for calling the PCA obj's underlying member method code with example usage: obj.row_coordinates(X) and returns: coord